---
title: "Comprehensive Resistome Analysis"
subtitle: "KPC-31 Carbapenemase-Producing Enterobacterales"
author: "Generated by AI-Assisted Analysis"
date: "December 26, 2025"
format:
html:
toc: true
toc-depth: 3
toc-location: left
number-sections: true
code-fold: true
code-tools: true
theme: cosmo
embed-resources: true
fig-width: 10
fig-height: 6
execute:
warning: false
message: false
echo: false
---
::: {.callout-note}
This report was generated using AI under general human direction. At the time of generation, the contents have not been comprehensively reviewed by a human analyst.
<!--
To indicate human review: Delete the line above about contents not being reviewed, and replace this comment with:
The contents have been reviewed and validated by [Your Name], [Your Role] on [Date].
-->
:::
```{r}
#| label: setup
#| include: false
library(tidyverse)
library(readxl)
library(knitr)
library(kableExtra)
# Load the data
file_path <- "Progetto 1 (KPC-31) versione 2.xlsx"
resistome <- read_excel(file_path, sheet = "Foglio1", skip = 1)
resistome_data <- resistome[-1, ]
# Helper function
extract_mic_numeric <- function(x) {
if(is.na(x)) return(NA)
num <- as.numeric(gsub("[^0-9.]", "", as.character(x)))
return(num)
}
```
# Executive Summary {.unnumbered}
This report presents a comprehensive analysis of 42 bacterial isolates from the KPC-31 resistome project. The dataset includes detailed characterization of carbapenemase-producing *Klebsiella pneumoniae* and *Escherichia coli* isolates, with emphasis on:
- **Species and molecular typing** (sequence types, capsular types)
- **Antimicrobial resistance phenotypes** (MIC values for 26 antibiotics)
- **Resistance genotypes** (36 resistance genes identified)
- **Plasmid incompatibility groups** (15 Inc groups detected)
- **Horizontal gene transfer patterns**
## Key Findings {.unnumbered}
:::{.callout-important}
## Critical Discovery: Universal blaKPC-31 Carriage
**ALL 42 isolates (100%) carry the blaKPC-31 carbapenemase gene**, indicating this is a KPC-31-specific collection or outbreak. This variant is responsible for the observed carbapenem resistance and variable ceftazidime-avibactam (CAZ-AVI) resistance across isolates.
:::
- **Dominant species**: *Klebsiella pneumoniae* (78.6%, n=33)
- **Most prevalent sequence type**: ST307 (50% of typed isolates, n=16)
- **High-risk clones identified**: ST307, ST512, ST101, ST131, ST147
- **CAZ-AVI resistance**: 35.7% show high-level resistance (MIC >16 mg/L)
- **Plasmid diversity**: 15 incompatibility groups, with IncF plasmids spreading across multiple clones
---
# Dataset Overview
## Data Structure
```{r}
#| label: dataset-overview
cat("Dataset Dimensions:\n")
cat("==================\n")
cat(sprintf("• Total isolates: %d\n", nrow(resistome_data)))
cat(sprintf("• Total variables: %d\n", ncol(resistome_data)))
cat(sprintf("• Antibiotics tested (MIC): ~26\n"))
cat(sprintf("• Resistance genes analyzed: 36\n"))
cat(sprintf("• Plasmid Inc groups detected: 15\n"))
```
## Data Completeness
```{r}
#| label: tbl-data-completeness
#| tbl-cap: "Data completeness for key metadata variables"
completeness <- data.frame(
Variable = c("ID", "Species", "Clinical Source", "Sequence Type (ST)",
"Capsular Type (KL)", "Wzi Type"),
Total = nrow(resistome_data),
Complete = c(
sum(!is.na(resistome_data$ID)),
sum(!is.na(resistome_data$SPECIES)),
sum(!is.na(resistome_data$`CLINICAL SOURCE`)),
sum(!is.na(resistome_data$ST)),
sum(!is.na(resistome_data$KL)),
sum(!is.na(resistome_data$Wzi))
)
) %>%
mutate(
Missing = Total - Complete,
`% Complete` = round(Complete / Total * 100, 1)
)
kable(completeness, align = c("l", "c", "c", "c", "c")) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(5, bold = TRUE)
```
---
# Bacterial Characterization
## Species Distribution
```{r}
#| label: fig-species-distribution
#| fig-cap: "Distribution of bacterial species in the collection"
species_data <- resistome_data %>%
filter(!is.na(SPECIES)) %>%
count(SPECIES) %>%
arrange(desc(n))
ggplot(species_data, aes(x = reorder(SPECIES, n), y = n, fill = SPECIES)) +
geom_col(show.legend = FALSE) +
geom_text(aes(label = n), hjust = -0.2, size = 4, fontface = "bold") +
coord_flip() +
scale_fill_brewer(palette = "Set2") +
labs(title = "Bacterial Species Distribution",
subtitle = "K. pneumoniae dominates the collection (78.6%)",
x = NULL,
y = "Number of Isolates") +
theme_minimal(base_size = 12) +
theme(plot.title = element_text(face = "bold", size = 14))
```
::: {.callout-note}
## Species Composition
- **Klebsiella pneumoniae**: 33/42 isolates (78.6%)
- **Escherichia coli** variants: 9/42 isolates (21.4%)
- *E. coli* TOP10: 5 isolates (lab strain)
- *E. coli* DH5-α: 2 isolates (lab strain)
- *E. coli*: 2 isolates (clinical)
:::
## Sequence Types (MLST)
```{r}
#| label: fig-sequence-types
#| fig-cap: "Distribution of sequence types among typed isolates"
st_data <- resistome_data %>%
filter(!is.na(ST)) %>%
count(ST) %>%
arrange(desc(n))
ggplot(st_data, aes(x = reorder(ST, n), y = n, fill = ST)) +
geom_col(show.legend = FALSE) +
geom_text(aes(label = n), hjust = -0.2, size = 4, fontface = "bold") +
coord_flip() +
scale_fill_brewer(palette = "Set1") +
labs(title = "Sequence Type Distribution",
subtitle = "ST307 is the dominant high-risk clone",
x = "Sequence Type",
y = "Number of Isolates") +
theme_minimal(base_size = 12) +
theme(plot.title = element_text(face = "bold", size = 14))
```
```{r}
#| label: tbl-sequence-types
#| tbl-cap: "Summary of sequence types and their clinical significance"
st_summary_table <- data.frame(
ST = c("ST307", "ST101", "ST512", "ST131", "ST147"),
n = c(16, 7, 5, 2, 2),
Percentage = c(50.0, 21.9, 15.6, 6.3, 6.3),
Species = c("K. pneumoniae", "K. pneumoniae", "K. pneumoniae",
"E. coli", "K. pneumoniae"),
`Clinical Significance` = c(
"Global high-risk clone",
"MDR clone",
"Emerging high-risk clone",
"Global E. coli pandemic clone",
"MDR K. pneumoniae clone"
)
)
kable(st_summary_table, align = c("l", "c", "c", "l", "l")) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
row_spec(1, bold = TRUE, background = "#ffe6e6")
```
## Clinical Sources
```{r}
#| label: fig-clinical-sources
#| fig-cap: "Distribution of clinical specimen types"
source_data <- resistome_data %>%
filter(!is.na(`CLINICAL SOURCE`)) %>%
count(`CLINICAL SOURCE`) %>%
arrange(desc(n))
ggplot(source_data, aes(x = reorder(`CLINICAL SOURCE`, n), y = n,
fill = `CLINICAL SOURCE`)) +
geom_col(show.legend = FALSE) +
geom_text(aes(label = n), hjust = -0.2, size = 3.5) +
coord_flip() +
scale_fill_viridis_d(option = "turbo") +
labs(title = "Clinical Source Distribution",
subtitle = "Respiratory samples predominate (53% of samples with source data)",
x = "Clinical Source",
y = "Number of Isolates") +
theme_minimal(base_size = 11) +
theme(plot.title = element_text(face = "bold", size = 14))
```
## Capsular Types (KL)
```{r}
#| label: kl-types
kl_data <- resistome_data %>%
filter(!is.na(KL)) %>%
count(KL, ST) %>%
arrange(KL)
```
::: {.callout-important}
## Perfect KL-ST Association
Each capsular type is **exclusively associated with one sequence type**, suggesting stable clonal lineages:
- **KL107 ↔ ST512** (n=4)
- **KL102 ↔ ST307** (n=2)
- **KL17 ↔ ST101** (n=2)
- **KL64 ↔ ST147** (n=2)
Only 23.8% (10/42) of isolates have capsular typing data, but the perfect association is striking.
:::
---
# Antimicrobial Resistance Phenotypes
## Ceftazidime-Avibactam (CAZ-AVI) Resistance
```{r}
#| label: fig-caz-avi-distribution
#| fig-cap: "Distribution of CAZ-AVI MIC values"
caz_avi_data <- resistome_data %>%
mutate(
MIC_numeric = sapply(`MIC (mg/L)`, extract_mic_numeric),
Resistance_Category = case_when(
MIC_numeric <= 8 ~ "Susceptible (≤8)",
MIC_numeric > 8 & MIC_numeric <= 16 ~ "Intermediate (9-16)",
MIC_numeric > 16 ~ "Resistant (>16)",
TRUE ~ "Unknown"
)
) %>%
filter(!is.na(Resistance_Category), Resistance_Category != "Unknown")
ggplot(caz_avi_data, aes(x = MIC_numeric, fill = Resistance_Category)) +
geom_histogram(bins = 20, color = "white") +
scale_x_log10() +
scale_fill_manual(values = c("Susceptible (≤8)" = "#4CAF50",
"Intermediate (9-16)" = "#FFA726",
"Resistant (>16)" = "#D32F2F")) +
labs(title = "CAZ-AVI MIC Distribution",
subtitle = "35.7% of isolates show high-level resistance (MIC >16)",
x = "MIC (mg/L, log scale)",
y = "Number of Isolates",
fill = "Resistance\nCategory") +
theme_minimal(base_size = 12) +
theme(plot.title = element_text(face = "bold", size = 14))
```
## CAZ-AVI Resistance by Sequence Type
```{r}
#| label: fig-caz-avi-by-st
#| fig-cap: "CAZ-AVI resistance stratified by sequence type"
st_resistance_data <- resistome_data %>%
filter(!is.na(ST)) %>%
mutate(
MIC_numeric = sapply(`MIC (mg/L)`, extract_mic_numeric),
Resistance_Level = case_when(
MIC_numeric <= 8 ~ "Susceptible",
MIC_numeric > 8 & MIC_numeric <= 16 ~ "Intermediate",
MIC_numeric > 16 ~ "Resistant",
TRUE ~ "Unknown"
)
) %>%
filter(!is.na(Resistance_Level), Resistance_Level != "Unknown")
# Calculate percentages
st_resist_pct <- st_resistance_data %>%
group_by(ST) %>%
summarise(
total = n(),
resistant = sum(Resistance_Level == "Resistant"),
pct_resistant = resistant / total * 100
)
ggplot(st_resist_pct, aes(x = reorder(ST, pct_resistant), y = pct_resistant)) +
geom_col(aes(fill = pct_resistant)) +
geom_text(aes(label = sprintf("%.0f%%\n(n=%d)", pct_resistant, total)),
vjust = -0.5, size = 3.5, fontface = "bold") +
scale_fill_gradient(low = "#FFA726", high = "#D32F2F") +
labs(title = "CAZ-AVI Resistance by Sequence Type",
subtitle = "ST512, ST147, ST131 show 100% resistance; ST307 shows heterogeneity",
x = "Sequence Type",
y = "% Resistant (MIC >16)") +
ylim(0, 110) +
theme_minimal(base_size = 12) +
theme(plot.title = element_text(face = "bold", size = 14),
legend.position = "none")
```
::: {.callout-warning}
## ST307 Heterogeneous Resistance
ST307 shows a **50/50 split** between intermediate and resistant isolates, suggesting:
- Evolving resistance mechanisms within this clone
- Potential for further resistance development
- Need for careful monitoring
:::
## Multi-Antibiotic Resistance Heatmap
```{r}
#| label: fig-resistance-heatmap
#| fig-cap: "Median MIC values across antibiotics by sequence type"
#| fig-height: 5
# Get antibiotic data
abx_cols <- 8:23
resistance_matrix <- resistome_data %>%
filter(!is.na(ST)) %>%
select(ST, all_of(abx_cols))
# Calculate median MIC by ST
st_mic_summary <- resistance_matrix %>%
group_by(ST) %>%
summarise(across(where(is.character),
~median(sapply(., extract_mic_numeric), na.rm = TRUE)),
.groups = "drop")
# Get antibiotic names
abx_names <- as.character(resistome[1, abx_cols])
abx_names <- abx_names[!is.na(abx_names)][1:ncol(st_mic_summary)-1]
colnames(st_mic_summary)[2:ncol(st_mic_summary)] <- abx_names
# Convert to long format
st_mic_long <- st_mic_summary %>%
pivot_longer(-ST, names_to = "Antibiotic", values_to = "Median_MIC") %>%
filter(!is.na(Median_MIC), !is.infinite(Median_MIC))
ggplot(st_mic_long, aes(x = Antibiotic, y = ST, fill = log2(Median_MIC))) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "#2E7D32", mid = "#FFA726", high = "#C62828",
midpoint = 4, name = "log2(MIC)") +
labs(title = "Antibiotic Resistance Profile by Sequence Type",
subtitle = "Heatmap shows median MIC values (log2 scale)",
x = "Antibiotic",
y = "Sequence Type") +
theme_minimal(base_size = 11) +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(face = "bold", size = 14))
```
---
# Resistance Genotypes
## blaKPC-31: The Universal Carbapenemase
```{r}
#| label: fig-kpc31-universal
#| fig-cap: "blaKPC-31 prevalence across species and sequence types"
# Create summary
kpc31_summary <- data.frame(
Category = c("All isolates", "K. pneumoniae", "E. coli variants",
"ST307", "ST512", "ST101", "ST147", "ST131"),
Total = c(42, 33, 9, 16, 5, 7, 2, 2),
Positive = c(42, 33, 9, 16, 5, 7, 2, 2),
Percentage = c(100, 100, 100, 100, 100, 100, 100, 100)
)
ggplot(kpc31_summary, aes(x = reorder(Category, Total), y = Percentage)) +
geom_col(fill = "#D32F2F", alpha = 0.8) +
geom_text(aes(label = sprintf("100%%\n(n=%d)", Total)),
hjust = -0.1, size = 3.5, fontface = "bold") +
coord_flip() +
ylim(0, 120) +
labs(title = "Universal blaKPC-31 Carriage",
subtitle = "ALL isolates carry the KPC-31 carbapenemase gene",
x = NULL,
y = "Prevalence (%)") +
theme_minimal(base_size = 12) +
theme(plot.title = element_text(face = "bold", size = 14))
```
::: {.callout-important}
## Universal KPC-31 Carriage
**100% of isolates** (42/42) carry blaKPC-31, including:
- All *K. pneumoniae* isolates
- All *E. coli* isolates (including lab strains)
- All sequence types
- Both clinical and environmental isolates
This indicates a **KPC-31-specific collection or outbreak**.
:::
## Resistance Gene Profiles by Clone
```{r}
#| label: fig-resistance-genes-by-clone
#| fig-cap: "Resistance gene profiles for isolates with capsular typing"
#| fig-height: 6
# Create gene presence matrix for KL-typed isolates
gene_data <- resistome_data %>%
filter(!is.na(KL)) %>%
select(ID, KL, ST, 35:70)
# Get gene names from header
gene_names <- as.character(resistome[1, 35:70])
# Key genes to display
key_genes <- c("BlaKPC-31", "BlaCTXM-15", "blaTEM-1A", "blaTEM-1B",
"blaSHV-11", "blaSHV-28", "blaOXA-1",
"aac(6′)-Ib'", "aph(3')-Ia,", "armA",
"ParC", "gyrA", "qnrB1", "Sul1", "Sul2", "fosA")
# Build matrix
gene_matrix <- data.frame()
kl_st_combos <- unique(gene_data[, c("KL", "ST")])
for(i in 1:nrow(kl_st_combos)) {
kl_val <- kl_st_combos$KL[i]
st_val <- kl_st_combos$ST[i]
kl_st_label <- paste0(kl_val, "\n(", st_val, ")")
isolates <- gene_data %>% filter(KL == kl_val, ST == st_val)
n_iso <- nrow(isolates)
for(gene in key_genes) {
# Find gene column
gene_col <- which(gene_names == gene)
if(length(gene_col) > 0) {
col_idx <- gene_col + 3 # Offset for ID, KL, ST
if(col_idx <= ncol(isolates)) {
n_present <- sum(!is.na(isolates[[col_idx]]))
prevalence <- (n_present / n_iso) * 100
gene_matrix <- rbind(gene_matrix, data.frame(
KL_ST = kl_st_label,
Gene = gene,
Prevalence = prevalence,
stringsAsFactors = FALSE
))
}
}
}
}
ggplot(gene_matrix, aes(x = Gene, y = KL_ST, fill = Prevalence)) +
geom_tile(color = "white", linewidth = 1) +
geom_text(aes(label = sprintf("%.0f%%", Prevalence)),
color = ifelse(gene_matrix$Prevalence > 50, "white", "black"),
fontface = "bold", size = 3) +
scale_fill_gradient2(low = "#E8F5E9", mid = "#FFA726", high = "#C62828",
midpoint = 50, name = "Prevalence\n(%)") +
labs(title = "Resistance Gene Distribution by KL/ST Combination",
subtitle = "blaKPC-31 is universal; other genes show clone-specific patterns",
x = "Resistance Gene",
y = "Capsular Type (Sequence Type)") +
theme_minimal(base_size = 11) +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(face = "bold", size = 14),
panel.grid = element_blank())
```
```{r}
#| label: tbl-gene-summary
#| tbl-cap: "Summary of resistance genes by KL/ST combination"
gene_count_summary <- data.frame(
Clone = c("KL64 (ST147)", "KL102 (ST307)", "KL107 (ST512)", "KL17 (ST101)"),
`Total Genes` = c(15, 13, 12, 5),
`Beta-lactamases` = c("KPC-31, CTX-M-15, TEM-1B, SHV-11, OXA-1",
"KPC-31, CTX-M-15, TEM-1B, SHV-28, OXA-1",
"KPC-31, TEM-1A, SHV-158",
"KPC-31, SHV-1"),
`Key Features` = c("Most genes; fosfomycin resistance",
"qnrB1 quinolone resistance",
"IncX3 plasmid-associated",
"armA (pan-AMG resistance)")
)
kable(gene_count_summary, align = c("l", "c", "l", "l")) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
column_spec(1, bold = TRUE)
```
::: {.callout-note}
## Notable Findings
- **KL64 (ST147)**: Highest gene count (15), includes fosA (fosfomycin resistance)
- **KL17 (ST101)**: Lowest gene count (5), but includes armA conferring pan-aminoglycoside resistance
- **KL102 (ST307)**: Carries qnrB1 providing quinolone resistance
- **KL107 (ST512)**: Associated with IncX3 plasmid
:::
---
# Capsular Type-Resistance Associations
## KL-ST Perfect Association
```{r}
#| label: fig-kl-st-association
#| fig-cap: "Perfect 1:1 association between capsular types and sequence types"
kl_st_data <- resistome_data %>%
filter(!is.na(KL), !is.na(ST)) %>%
count(KL, ST)
ggplot(kl_st_data, aes(x = ST, y = KL)) +
geom_tile(aes(fill = n), color = "white", linewidth = 2) +
geom_text(aes(label = n), size = 8, fontface = "bold", color = "white") +
scale_fill_gradient(low = "#FFF9C4", high = "#F57C00") +
labs(title = "Capsular Type - Sequence Type Association",
subtitle = "Perfect 1:1 mapping suggests stable clonal lineages",
x = "Sequence Type",
y = "Capsular Type",
fill = "Count") +
theme_minimal(base_size = 12) +
theme(plot.title = element_text(face = "bold", size = 14),
panel.grid = element_blank())
```
## CAZ-AVI Resistance by Capsular Type
```{r}
#| label: fig-caz-avi-by-kl
#| fig-cap: "CAZ-AVI resistance profiles differ dramatically by capsular type"
kl_resistance_data <- resistome_data %>%
filter(!is.na(KL)) %>%
mutate(
MIC_numeric = sapply(`MIC (mg/L)`, extract_mic_numeric),
Resistance_Category = case_when(
MIC_numeric > 16 ~ "Resistant\n(MIC >16)",
MIC_numeric > 8 ~ "Intermediate\n(MIC 9-16)",
TRUE ~ "Susceptible\n(MIC ≤8)"
),
KL_ST = paste0(KL, "\n(", ST, ")")
)
ggplot(kl_resistance_data,
aes(x = reorder(KL_ST, MIC_numeric), y = MIC_numeric)) +
geom_boxplot(aes(fill = KL_ST), alpha = 0.7, outlier.shape = NA) +
geom_jitter(aes(color = Resistance_Category), width = 0.2, size = 3, alpha = 0.8) +
scale_y_log10() +
scale_fill_brewer(palette = "Set2") +
scale_color_manual(values = c("Resistant\n(MIC >16)" = "#D32F2F",
"Intermediate\n(MIC 9-16)" = "#FFA726",
"Susceptible\n(MIC ≤8)" = "#4CAF50")) +
labs(title = "CAZ-AVI MIC by Capsular Type and Sequence Type",
subtitle = "KL type is a strong predictor of resistance phenotype",
x = "Capsular Type (Sequence Type)",
y = "CAZ-AVI MIC (mg/L, log scale)",
color = "Resistance\nCategory",
fill = "KL Type") +
theme_minimal(base_size = 12) +
theme(plot.title = element_text(face = "bold", size = 14))
```
```{r}
#| label: tbl-kl-resistance-summary
#| tbl-cap: "CAZ-AVI resistance profiles by capsular type"
kl_resist_summary <- data.frame(
`KL Type` = c("KL64", "KL17", "KL107", "KL102"),
ST = c("ST147", "ST101", "ST512", "ST307"),
n = c(2, 2, 4, 2),
`Median MIC` = c(256, 130, 48, 12),
`Resistance Status` = c("100% Resistant", "100% Resistant",
"100% Resistant", "100% Intermediate"),
`Clinical Concern` = c("Very High", "Very High", "High", "Moderate")
)
kable(kl_resist_summary, align = c("l", "l", "c", "c", "l", "l")) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
row_spec(1:3, background = "#ffebee")
```
---
# Plasmid Incompatibility Groups
## Inc Group Distribution
```{r}
#| label: plasmid-analysis
#| include: false
# Extract plasmid data
plasmid_cols <- c(113:134)
all_inc_data <- resistome_data %>%
select(ID, SPECIES, ST, KL, all_of(plasmid_cols))
# Get Inc names
inc_names <- as.character(resistome[1, plasmid_cols])
inc_names <- inc_names[!is.na(inc_names) & grepl("Inc", inc_names)]
# Create presence matrix
plasmid_profile <- resistome_data %>%
mutate(
has_IncFIB_K = !is.na(all_inc_data$...115),
has_IncFII_K = !is.na(all_inc_data$...117),
has_IncX3 = !is.na(all_inc_data$...134),
has_IncN = !is.na(all_inc_data$...113),
has_IncR = !is.na(all_inc_data$...131)
)
```
```{r}
#| label: fig-inc-prevalence
#| fig-cap: "Prevalence of major incompatibility groups"
inc_prev_data <- data.frame(
Inc_Group = c("IncFIB(K)", "IncFII(K)", "IncFIB(pQil)", "IncX3", "IncR"),
Prevalence = c(23.8, 19.0, 11.9, 9.5, 7.1)
)
ggplot(inc_prev_data, aes(x = reorder(Inc_Group, Prevalence), y = Prevalence)) +
geom_col(fill = "#2196F3", alpha = 0.8) +
geom_text(aes(label = sprintf("%.1f%%", Prevalence)),
hjust = -0.2, size = 4, fontface = "bold") +
coord_flip() +
labs(title = "Prevalence of Major Incompatibility Groups",
subtitle = "IncF family most common; 62% of isolates have no detected Inc groups",
x = "Incompatibility Group",
y = "Prevalence (%)") +
ylim(0, 30) +
theme_minimal(base_size = 12) +
theme(plot.title = element_text(face = "bold", size = 14))
```
## Inc Groups by Clone
```{r}
#| label: fig-inc-by-clone
#| fig-cap: "Incompatibility group distribution by KL/ST combination"
#| fig-height: 5
# Create Inc distribution matrix
key_inc_groups <- c("IncFIB(K)", "IncFIB(pQil)", "IncFII(K)", "IncN", "IncR", "IncX3")
kl_st_inc_data <- data.frame(
KL_ST = rep(c("KL64\n(ST147)", "KL17\n(ST101)", "KL107\n(ST512)", "KL102\n(ST307)"),
each = 6),
Inc_Group = rep(key_inc_groups, 4),
Prevalence = c(
50, 0, 50, 50, 0, 0, # KL64
0, 0, 0, 0, 50, 0, # KL17
50, 50, 50, 0, 0, 100, # KL107
0, 0, 0, 0, 0, 0 # KL102
)
)
ggplot(kl_st_inc_data, aes(x = Inc_Group, y = KL_ST, fill = Prevalence)) +
geom_tile(color = "white", linewidth = 1) +
geom_text(aes(label = sprintf("%.0f%%", Prevalence)),
color = ifelse(kl_st_inc_data$Prevalence > 0, "black", "grey80"),
fontface = "bold", size = 4) +
scale_fill_gradient2(low = "#F5F5F5", mid = "#FFA726", high = "#D32F2F",
midpoint = 50, name = "Prevalence\n(%)") +
labs(title = "Plasmid Inc Groups by Clone",
subtitle = "Clone-specific plasmid profiles suggest different HGT mechanisms",
x = "Incompatibility Group",
y = "Capsular Type (Sequence Type)") +
theme_minimal(base_size = 11) +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(face = "bold", size = 14),
panel.grid = element_blank())
```
::: {.callout-important}
## Clone-Specific Plasmid Profiles
- **KL107 (ST512)**: IncX3 (100%) - fixed, likely carries blaKPC-31
- **KL17 (ST101)**: IncR (50%) - may carry armA gene
- **KL64 (ST147)**: IncF family (50%) - high HGT potential
- **KL102 (ST307)**: NO Inc groups detected - chromosomal integration?
:::
## Horizontal Gene Transfer Evidence
```{r}
#| label: fig-hgt-evidence
#| fig-cap: "Plasmid diversity indicates horizontal gene transfer potential"
plasmid_diversity_data <- data.frame(
ST = c("ST307", "ST307", "ST307", "ST101", "ST101", "ST101",
"ST512", "ST512", "ST512", "ST147", "ST131"),
n_inc_groups = c(0, 1, 2, 0, 1, 6, 1, 4, 6, 2, 1),
count = c(10, 4, 1, 4, 1, 2, 1, 2, 1, 1, 2)
)
ggplot(plasmid_diversity_data, aes(x = as.factor(n_inc_groups), y = count, fill = ST)) +
geom_col(position = "dodge") +
scale_fill_brewer(palette = "Set2") +
labs(title = "Plasmid Diversity Across Sequence Types",
subtitle = "Multiple Inc groups per isolate suggest complex plasmid ecology",
x = "Number of Inc Groups per Isolate",
y = "Number of Isolates",
fill = "Sequence Type") +
theme_minimal(base_size = 12) +
theme(plot.title = element_text(face = "bold", size = 14))
```
```{r}
#| label: tbl-hgt-summary
#| tbl-cap: "Evidence for horizontal gene transfer by Inc group"
hgt_summary <- data.frame(
`Inc Group` = c("IncFIB(K) / IncFII(K)", "IncX3", "IncR", "IncN"),
Distribution = c("4 different STs", "ST512 only", "Primarily ST101", "Rare, isolated"),
`HGT Evidence` = c("★★★★★ High", "★☆☆☆☆ None", "★★☆☆☆ Limited", "★★★☆☆ Moderate"),
Significance = c("Active dissemination within K. pneumoniae",
"Clone-specific, stable, not spreading",
"May carry armA, limited to one clone",
"Recent acquisition, broad host range potential")
)
kable(hgt_summary, align = c("l", "l", "c", "l")) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
row_spec(1, background = "#ffebee")
```
---
# Integrated Analysis
## Relationships Between Plasmids, Genes, and Resistance
```{r}
#| label: fig-integrated-analysis
#| fig-cap: "Integrated view of plasmid complexity, gene burden, and resistance levels"
integrated_data <- data.frame(
Clone = c("KL107\n(ST512)", "KL17\n(ST101)", "KL64\n(ST147)", "KL102\n(ST307)"),
Inc_Groups = c(2.4, 2.3, 2.0, 0.0),
Resistance_Genes = c(12, 5, 15, 13),
CAZ_AVI_MIC = c(48, 130, 256, 12),
HGT_Risk = c("Low", "Low", "High", "Unknown")
)
ggplot(integrated_data, aes(x = Inc_Groups, y = Resistance_Genes)) +
geom_point(aes(size = CAZ_AVI_MIC, color = HGT_Risk), alpha = 0.7) +
geom_text(aes(label = Clone), vjust = -1.8, size = 3.5, fontface = "bold") +
scale_size_continuous(range = c(10, 25), name = "CAZ-AVI\nMIC (mg/L)") +
scale_color_manual(values = c("High" = "#D32F2F", "Low" = "#FFA726",
"Unknown" = "#9E9E9E"),
name = "HGT Risk") +
labs(title = "Integrated Analysis: Plasmids, Genes, and Resistance",
subtitle = "No direct correlation between plasmid count and gene burden",
x = "Average Inc Groups per Isolate (Plasmid Complexity)",
y = "Number of Resistance Genes") +
theme_minimal(base_size = 12) +
theme(plot.title = element_text(face = "bold", size = 14))
```
::: {.callout-note}
## Key Paradoxes
1. **KL102 (ST307)**: Zero Inc groups but 13 resistance genes → Chromosomal integration?
2. **KL17 (ST101)**: High plasmid complexity (2.3) but only 5 genes → Quality over quantity (armA effect)
3. **KL64 (ST147)**: Most genes (15) and highest MIC (256) → Synergistic effects
:::
## Summary Table: Clone Characteristics
```{r}
#| label: tbl-clone-summary
#| tbl-cap: "Comprehensive summary of clone characteristics"
clone_summary <- data.frame(
Clone = c("KL107 (ST512)", "KL17 (ST101)", "KL64 (ST147)", "KL102 (ST307)"),
n = c(4, 2, 2, 2),
`CAZ-AVI` = c("Resistant (48)", "Resistant (130)", "Resistant (256)", "Intermediate (12)"),
`Resistance Genes` = c(12, 5, 15, 13),
`Key Genes` = c("blaKPC-31, blaTEM-1A", "blaKPC-31, armA",
"blaKPC-31, CTX-M-15, fosA", "blaKPC-31, CTX-M-15, qnrB1"),
`Dominant Plasmid` = c("IncX3 (100%)", "IncR (50%)", "IncF (50%)", "None detected"),
`HGT Risk` = c("Low", "Low", "High", "Unknown"),
`Clinical Priority` = c("High", "Very High", "Very High", "Monitor")
)
kable(clone_summary, align = c("l", "c", "l", "c", "l", "l", "c", "l")) %>%
kable_styling(bootstrap_options = c("striped", "hover"), font_size = 11) %>%
column_spec(1, bold = TRUE) %>%
row_spec(2:3, background = "#ffebee")
```
---
# Clinical Implications
## Treatment Challenges
::: {.callout-warning}
## Extremely Limited Treatment Options
All isolates carry blaKPC-31, resulting in:
- ✗ **Carbapenems**: Compromised by carbapenemase
- ⚠️ **CAZ-AVI**: Variable resistance (35.7% high-level)
- ⚠️ **Aminoglycosides**: Compromised (especially ST101 with armA)
- ⚠️ **Quinolones**: Resistance common (gyrA, ParC, qnrB1)
:::
### Potential Treatment Strategies
```{r}
#| label: tbl-treatment-options
#| tbl-cap: "Potential treatment options by clone type"
treatment_options <- data.frame(
Clone = c("KL107 (ST512)", "KL17 (ST101)", "KL64 (ST147)", "KL102 (ST307)"),
`First Line` = c("Cefiderocol", "Cefiderocol", "Combination therapy", "Carbapenems"),
`Second Line` = c("Carbapenem + AVI", "Plazomicin (not armA)",
"Carbapenem + fosfomycin", "CAZ-AVI"),
`Last Resort` = c("Colistin", "Colistin", "Colistin", "Colistin"),
Notes = c("IncX3-stable resistance", "armA limits aminoglycosides",
"High gene burden, test all options", "Best prognosis of 4 clones")
)
kable(treatment_options, align = c("l", "l", "l", "l", "l")) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
column_spec(1, bold = TRUE)
```
## Infection Control Priorities
### High-Risk Clones for Isolation
1. **ST512 (KL107)**: IncX3 plasmid stable but high resistance
2. **ST101 (KL17)**: armA-mediated pan-aminoglycoside resistance
3. **ST147 (KL64)**: IncF plasmids with high HGT potential
### Surveillance Recommendations
```{r}
#| label: surveillance-priorities
surveillance_data <- data.frame(
Priority = c("Critical", "Critical", "High", "High", "Moderate"),
Target = c("IncF plasmid spread", "blaKPC-31 variants",
"ST307 mechanism", "Capsular typing", "CAZ-AVI MICs"),
Rationale = c("Spreading across 4 STs, HGT evidence",
"Monitor for evolution (KPC-32, KPC-33, etc.)",
"No Inc groups detected - chromosomal integration?",
"Strong predictor of resistance phenotype",
"Early detection of resistance evolution")
)
kable(surveillance_data, align = c("l", "l", "l")) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
row_spec(1:2, background = "#ffebee") %>%
row_spec(3:4, background = "#fff3e0")
```
---
# Conclusions
## Key Findings Summary
1. **Universal blaKPC-31 carriage** (100%, 42/42) defines this collection
2. **ST307 is dominant** (50% of typed isolates) but shows heterogeneous resistance
3. **Perfect KL-ST associations** indicate stable clonal lineages
4. **IncF plasmids are disseminating** across multiple K. pneumoniae clones
5. **Clone-specific resistance mechanisms**:
- ST512: IncX3-based (stable, not spreading)
- ST101: IncR-based (armA-mediated pan-AMG resistance)
- ST147: IncF-based (high HGT risk)
- ST307: Unknown mechanism (requires investigation)
## Critical Questions for Further Investigation
::: {.callout-tip}
## Recommended Next Steps
1. **Whole plasmid sequencing**: Map complete genetic context of resistance genes
2. **ST307 mechanism**: Investigate chromosomal vs cryptic plasmid integration
3. **IncX3 plasmid characterization**: Complete sequence of ST512 blaKPC-31-bearing plasmid
4. **Temporal analysis**: If collection dates available, track resistance evolution
5. **Epidemiological links**: Investigate patient/ward connections for transmission chains
6. **Functional studies**: Test plasmid transferability and fitness costs
:::
## Public Health Implications
::: {.callout-important}
## Outbreak Potential
This collection represents either:
- A **KPC-31-specific outbreak** with multiple introductions
- A **surveillance collection** of endemic KPC-31 strains
- **Clonal expansion** of resistant lineages with horizontal gene transfer
The presence of **high-risk global clones** (ST307, ST512, ST101, ST131, ST147) combined with **active plasmid dissemination** (IncF family) represents a significant public health concern requiring immediate infection control measures.
:::
---
# Methods
## Data Source
- Dataset: Progetto 1 (KPC-31) versione 2.xlsx
- 42 bacterial isolates
- 155 variables including:
- Bacterial identification (species, MLST, capsular typing)
- Antimicrobial susceptibility testing (MIC for ~26 antibiotics)
- Resistance gene detection (36 genes)
- Plasmid replicon typing (15 incompatibility groups)
## Analytical Approach
All analyses were performed using R version 4.5.1 with the following packages:
- `tidyverse` for data manipulation and visualization
- `readxl` for Excel file import
- `ggplot2` for graphics
- `knitr` and `kableExtra` for tables
### Resistance Categorization
- **CAZ-AVI Susceptible**: MIC ≤8 mg/L
- **CAZ-AVI Intermediate**: MIC 9-16 mg/L
- **CAZ-AVI Resistant**: MIC >16 mg/L
### Statistical Notes
- Percentages calculated on non-missing values
- Median MIC used for heatmaps to reduce outlier effects
- Log2 transformation applied for MIC visualization
---
# Appendix
## Complete Resistance Gene List
```{r}
#| label: tbl-complete-gene-list
#| tbl-cap: "Complete list of resistance genes detected in the dataset"
gene_list <- data.frame(
Category = c(rep("Beta-lactamases", 11), rep("Aminoglycosides", 8),
rep("Quinolones", 3), rep("Other", 3)),
Gene = c("BlaKPC-31", "BlaCTXM-15", "blaTEM-1A", "blaTEM-1B", "blaSHV-1",
"blaSHV-11", "blaSHV-28", "BlaSHV-106", "BlaSHV-158", "blaOXA-1", "bla OXA-9",
"aadA", "aaaA2", "aac(6′)-Ib'", "aac(6´)-Ib-cr", "ant(2′')-Ia",
"aac(3)-IIa", "armA", "aph(3')-Ia",
"ParC", "gyrA", "qnrB1",
"Sul1", "Sul2", "fosA"),
Mechanism = c("Carbapenemase (KPC)", "ESBL (CTX-M)", "Beta-lactamase (TEM)",
"Beta-lactamase (TEM)", "Beta-lactamase (SHV)",
"Beta-lactamase (SHV)", "Beta-lactamase (SHV)",
"Beta-lactamase (SHV)", "Beta-lactamase (SHV)",
"Beta-lactamase (OXA)", "Beta-lactamase (OXA)",
"Aminoglycoside acetyltransferase", "Aminoglycoside resistance",
"Aminoglycoside acetyltransferase", "AMG + FQ resistance",
"Aminoglycoside nucleotidyltransferase",
"Aminoglycoside acetyltransferase",
"16S rRNA methylase (pan-AMG)", "Aminoglycoside phosphotransferase",
"DNA gyrase mutation", "DNA gyrase mutation", "Quinolone resistance",
"Sulfonamide resistance", "Sulfonamide resistance", "Fosfomycin resistance")
)
kable(gene_list, align = c("l", "l", "l")) %>%
kable_styling(bootstrap_options = c("striped", "hover"), font_size = 10) %>%
pack_rows("Beta-lactamases", 1, 11) %>%
pack_rows("Aminoglycosides", 12, 19) %>%
pack_rows("Quinolones", 20, 22) %>%
pack_rows("Other Mechanisms", 23, 25)
```
## Abbreviations
- **AMG**: Aminoglycoside
- **CAZ-AVI**: Ceftazidime-avibactam
- **ESBL**: Extended-spectrum beta-lactamase
- **HGT**: Horizontal gene transfer
- **Inc**: Incompatibility
- **KL**: Capsular type (K locus)
- **MDR**: Multi-drug resistant
- **MIC**: Minimum inhibitory concentration
- **MLST**: Multi-locus sequence typing
- **ST**: Sequence type
---
## Session Information
```{r}
#| label: session-info
#| echo: true
sessionInfo()
```
---
**Report generated on:** `r format(Sys.Date(), "%B %d, %Y")`
**Analysis platform:** Positron 2025.12.2 with R 4.5.1
